



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_		 =					logging.get_logger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     str:
   _A      =       RobertaPreLayerNormConfig.from_pretrained(
       _snake_case	,   architectures=['''RobertaPreLayerNormForMaskedLM''']				)

   # convert state_dict
   _A      =       torch.load(hf_hub_download(repo_id=_snake_case	,   filename='''pytorch_model.bin'''				)				)
   _A      =       {}
   for tensor_key, tensor_value in original_state_dict.items():
      # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
      if tensor_key.startswith('''roberta.'''				):
         _A      =       '''roberta_prelayernorm.''' + tensor_key[len('''roberta.'''				) :]

      # The original implementation contains weights which are not used, remove them from the state_dict
      if tensor_key.endswith('''.self.LayerNorm.weight'''				) or tensor_key.endswith('''.self.LayerNorm.bias'''				):
         continue

      _A      =       tensor_value

   _A      =       RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=_snake_case	,   config=_snake_case	,   state_dict=_snake_case				)
   model.save_pretrained(_snake_case				)

   # convert tokenizer
   _A      =       AutoTokenizer.from_pretrained(_snake_case				)
   tokenizer.save_pretrained(_snake_case				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint-repo""",
       default=None,
       type=str,
       required=True,
       help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   UpperCAmelCase_		 =					parser.parse_args()
   convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




